Introduction to MCP: Making AI More Connected

With the rising capabilities of Large Language Models(LLMs) such as ChatGPT, Claude, Gemini, The AI ecosystem is changing rapidly. These models are often limited by their training data and don't have access to real-time data or specialized tools. That’s where MCP, or Model Context Protocol, comes in.

In this blog, we’ll break down what MCP is, why it’s useful, and how it helps AI work better with the tools and data we already use.

So what is MCP?

MCP (Model Context Protocol) is a new open standard that helps AI models interact with external tools and data in a structured, secure, and consistent way.

Imagine this:

You're chatting with an AI and you ask:

"Can you summarize the latest file in my Downloads folder?"

Without MCP, the AI wouldn’t have access to that file.

With MCP, the AI can ask an external tool (called a “Server”) for help, get the file, and provide the summary — all behind the scenes.

What Problem does MCP Solves

It helps solve the M×N Integration Problem. It refers to the challenge of connecting M different AI applications to N different tools or data sources without a standardized approach.
Take for example let’s say we have:

6 different AI models
10 different tools (weather APIs, databases, calculators, file readers, etc.)

Without a shared protocol, you'd need 6 × 10 = 60 custom integrations.

Talk about a maintenance nightmare 😫 !

MCP simplifies this by transforming it to an M + N problem using a single, shared protocol. So:

Tools only need to implement server side of MCP once
Each AI application implements the client side of MCP once
AI Hosts that support MCP can instantly connect This drastically reduces the integration complexity and maintenance problem.

Key MCP Concepts

Let’s break down some important terms:

Term	Description
Host	The AI application or product users interact with (e.g., chatbot, IDE). They initiate the connections to MCP Servers and orchestrate the overall flow between user requests, LLM processing, and external tools
Client	A component in the Host that talks to a specific MCP Server. Each client maintains a 1:1 connection with a server and handles the protocol-level details of MCP communication and acts as an intermediary between the Host’s logic and the external Server
Server	A tool or service that exposes capabilities (can be Tools, Resources, Prompts) via MCP protocol
Tool	Functions that Ai model can invoke to perform specific actions. e.g. A python Code executor tool helps AI model to execute python code and return the result.
Resource	Read-only data like documents or files that provide context to models.
Prompt	A predefined text-based instruction the AI can use. e.g. A Summarization prompt
Sampling	Server-initiated requests let the AI run itself again to review and improve its own work. e.g. The AI writes some code, then the server asks it to run again to check if the code works and fix any errors.
MCP URI	A special format to identify tools and capabilities (e.g. `mcp://tools/python_executor/run_python_code`)

How MCP is Built: The Architecture

MCP follows a clear architecture made of three layers:

1. Host Application

This is the AI-powered app you're using — like a coding assistant or smart chatbot. It includes:

The Model (e.g., an LLM)
A Client, which talks to external Servers via MCP

2. Client Layer

Think of the Client as a translator.
It speaks the MCP language and handles communication between the AI (Host) and tools (Servers).

The Client does things like:

Registering available tools and capabilities
Routing the AI’s requests to the right Server
Handling inputs/outputs securely

3. Server Layer

These are the actual tools and services that do the work.
Servers define one or more tools (like Python runners, file searchers, or translators).
Each tool offers capabilities, which the AI models can use.

A Real Example: Using a PDF Summarizer Tool

Let’s say you ask:

“Can you summarize the contents of my meeting_notes.pdf file?”

Here’s what happens:

Host (AI app) receives your request to summarize a PDF
It forwards the request to the Client
The Client calls the Server that exposes the summarize_pdf capability
The Server reads the PDF file and generates a summary
The Host includes that summary in the AI’s response

And just like that — your AI becomes a PDF summarizer!

Why MCP Matters

Here’s why MCP is a game-changer:

✅ Standardized – Write once, use anywhere
✅ Interoperable – Connect different tools to different AIs easily
✅ Modular – Add/remove tools without breaking things
✅ Flexible – Works locally or remotely
✅ Scalable – No need for N × M integrations anymore

Capabilities Types in MCP

There are 3 main capability types in MCP:

Tool: Runs actions like executing code or searching files
Resource: Read-only, like a document or file the model can view
Prompt: Template instructions to guide the AI’s responses
Sampling: Server-initiated requests let the AI model run itself again to review and improve its own work.

In the following diagram we can see the collective capabilities for the use case of a pdf summarizer.

Final Thoughts

MCP is a powerful way to connect AI with the real world — in a safe, simple, and scalable manner. Whether you’re building smart assistants, data dashboards, or developer tools, MCP can make your AI much more capable.

We’re just scratching the surface — the future of AI will be connected, and MCP is helping lead the way.